diff --git "a/PaliGemma_DPO.ipynb" "b/PaliGemma_DPO.ipynb"
new file mode 100644--- /dev/null
+++ "b/PaliGemma_DPO.ipynb"
@@ -0,0 +1,14221 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "X3HSf7KnG48q"
+ },
+ "source": [
+ "## PaliGemma π€ Direct Preference Optimization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "emG7uw6IdhFs",
+ "outputId": "07ce2f98-6c3d-47b5-eca5-2d58271dce9e"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -q git+https://github.com/huggingface/transformers.git"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "XhqsZVjzDmt4",
+ "outputId": "5fbc28d8-76aa-4f12-a97f-b00db5ff0e6d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m314.1/314.1 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m103.4/103.4 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m51.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m28.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m24.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Building wheel for trl (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
+ "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
+ "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0m"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -q git+https://github.com/qgallouedec/trl.git@main"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "nDXtvLWPTrbv",
+ "outputId": "6b9a509a-4c0c-4928-ce59-1b697c32dfce"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m251.6/251.6 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -q -U peft accelerate bitsandbytes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VibvptIEHkuY"
+ },
+ "source": [
+ "Let's load the model and the processor. First we need to login using `notebook_login`Β since PaliGemma is a gated model. For utmost security, you can create a fine-grained token with access to PaliGemma and pass that."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
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+ "height": 145,
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+ },
+ "id": "ulMkCsP7IY2K",
+ "outputId": "8aa73adf-f6ae-4742-e84b-b4c092b6cdad"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='
{% endif %}{% endfor %}{% if message['role'] == 'user' %} {% else %}{{eos_token}}{% endif %}{% endfor %}{% if add_generation_prompt %}ASSISTANT: {% endif %}\"\"\"\n",
+ "\n",
+ "\n",
+ "def process(row):\n",
+ " row[\"prompt\"] = processor.apply_chat_template(row[\"prompt\"], tokenize=False)\n",
+ " row[\"chosen\"] = processor.apply_chat_template(row[\"chosen\"], tokenize=False)\n",
+ " row[\"rejected\"] = processor.apply_chat_template(row[\"rejected\"], tokenize=False)\n",
+ " row[\"images\"][0] = row[\"images\"][0].convert(\"RGB\")\n",
+ " return row"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6a8v6qEdMkLt"
+ },
+ "source": [
+ "We'll do a very small run (as this notebook is made for education purposes)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 137,
+ "referenced_widgets": [
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+ },
+ "id": "5405q6r-L_je",
+ "outputId": "b82fa2e7-f658-4c36-f6fe-c149c42635b3"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+ " self.pid = os.fork()\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map (num_proc=8): 0%| | 0/39487 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "42d1923c477344c9926c9f383c2fbaa5"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map (num_proc=8): 0%| | 0/4157 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "84d162fd75074e0db85881fb4c147904"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "train_dataset = ds[\"train\"].train_test_split(test_size=0.5)[\"train\"]\n",
+ "eval_dataset = ds[\"test\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "q2eHSY7TZoR1"
+ },
+ "outputs": [],
+ "source": [
+ "from trl import DPOConfig, DPOTrainer, ModelConfig\n",
+ "\n",
+ "training_args = DPOConfig(output_dir=\"/content/outs\",\n",
+ " per_device_train_batch_size=8,\n",
+ " num_train_epochs=1)\n",
+ "\n",
+ "trainer = DPOTrainer(\n",
+ " model,\n",
+ " model_ref,\n",
+ " args=training_args,\n",
+ " train_dataset=train_dataset,\n",
+ " eval_dataset=eval_dataset,\n",
+ " tokenizer=processor,\n",
+ " dataset_num_proc=32,\n",
+ " # uncomment for peft\n",
+ " # peft_config=get_peft_config(model_config)\n",
+ ")\n",
+ "trainer.train()\n",
+ "\n",
+ "trainer.save_model(training_args.output_dir)"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "A100",
+ "machine_shape": "hm",
+ "provenance": []
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